Abstract:  Traffic accidents result in significant losses to individuals and society. To enhance the accuracy of traffic accident prediction， a traffic accident prediction method based on graph attention and graph convolutional networks (GAGC) is proposed. Firstly， the method extracts complex edge feature information in the road network through an edge feature extraction module. Then， it introduces a graph attention layer to enable the network quickly focusing on nodes with frequent accidents， and uses overlapping graph attention layers to reduce information loss during feature information transmission. It also employs Dropout and Batch Normalization (BN) to balance feature importance and improve the generalization and robustness of the model. Experimental results show that GAGC achieves good results， and the model can fully consider the geospatial features in complex road networks， with better performance than five baseline models in terms of F1 index， AUC， and MAP. The ablation experiment further verifies the effectiveness and reliability of the GAGC model designed in this study.

Key words: graph convolutional network, graph attention, road traffic, traffic accident prediction